Introduction An estimated 803 900 people worldwide died as a result of self-harm in 2012. The deliberate ingestion of pesticides has been identified as the method most frequently used to commit fatal self-harm globally. In Sri Lanka, it is estimated that up to 60% of all suicides are committed using this method. The aim of the present study is to assess the cost-effectiveness of an ongoing safe storage intervention currently taking place in a rural Sri Lankan district and to model the cost-effectiveness of implementing the safe storage intervention as well as four potential interventions (legislative, medical management, follow-up contact and mobile phone contact) on a national level. Methods and analysis Study design for all the strategies is a cost-effectiveness analysis. A governmental perspective is adopted. The time horizon for tracking the associated costs and health outcomes of the safe storage intervention on district level runs over 3 years. The time horizon is extended to 5 years when modelling a full national roll-out of the respective interventions. The discounting of costs and health outcomes are undertaken at the recommended real rate of 3%. Threshold analyses of the modelled strategies are employed to assess the strategies potential for cost-effectiveness, running scenarios with health outcome improvements ranging from 1% to 100%. Sensitivity analyses are also performed. The main outcome measures of the safe storage intervention are incremental cost-effectiveness ratios. Ethics and dissemination Ethical approval was granted for the safe storage project from the University of Peradeniya, Sri Lanka, in March of 2008. An amendment for the present study was granted from Rajarata University of Sri Lanka in November of 2013. Findings will be disseminated to public and private stakeholders in local and national government in Sri Lanka as well as the wider academic audience through peer-reviewed publications and international conferences. Trial registration number The safe storage cluster trial is registered with the Clinical Trials, ref: NCT1146496 (http://clinicaltrialsfeeds.org/clinical-trials/show/NCT1146496).

The UK introduced an ambitious national strategy to reduce population levels of salt intake in 2003. The aim of this study was to evaluate the impact of this strategy on salt intake in England, including potential effects on health inequalities. Secondary analysis of data from the Health Survey for England. Our main outcome measure was trends in estimated daily salt intake from 2003–2007, as measured by spot urine. Secondary outcome measures were knowledge of government guidance and voluntary use of salt in food preparation over this time period. There were significant reductions in salt intake between 2003 and 2007 (−0.175grams per day per year, p<0.001). Intake decreased uniformly across all other groups but remained significantly higher in younger persons, men, ethnic minorities and lower social class groups and those without hypertension in 2007. Awareness of government guidance on salt use was lowest in those groups with the highest intake (semi-skilled manual v professional; 64.9% v 71.0% AOR 0.76 95% CI 0.58–0.99). Self reported use of salt added at the table reduced significantly during the study period (56.5% to 40.2% p<0.001). Respondents from ethnic minority groups remained significantly more likely to add salt during cooking (white 42.8%, black 74.1%, south Asian 88.3%) and those from lower social class groups (unskilled manual 46.6%, professional 35.2%) were more likely to add salt at the table. The introduction a national salt reduction strategy was associated with uniform but modest reductions in salt intake in England, although it is not clear precisely which aspects of the strategy contributed to this. Knowledge of government guidance was lower and voluntary salt use and total salt intake was higher among occupational and ethnic groups at greatest risk of cardiovascular disease.

Objective To explore the quality and safety of patients’ healthcare provision by identifying whether being a medical outlier is associated with worse patient outcomes. A medical outlier is a hospital inpatient who is classified as a medical patient for an episode within a spell of care and has at least one non-medical ward placement within that spell. Data sources Secondary data from the Patient Administration System of a district general hospital were provided for the financial years 2013/2014–2015/2016. The data included 71 038 medical patient spells for the 3-year period. Study design This research was based on a retrospective, cross-sectional observational study design. Multivariate logistic regression and zero-truncated negative binomial regression were used to explore patient outcomes (in-hospital mortality, 30-day mortality, readmissions and length of stay (LOS)) while adjusting for several confounding factors. Principal findings Univariate analysis indicated that an outlying medical in-hospital patient has higher odds for readmission, double the odds of staying longer in the hospital but no significant difference in the odds of in-hospital and 30-day mortality. Multivariable analysis indicates that being a medical outlier does not affect mortality outcomes or readmission, but it does prolong LOS in the hospital. Conclusions After adjusting for other factors, medical outliers are associated with an increased LOS while mortality or readmissions are not worse than patients treated in appropriate specialty wards. This is in line with existing but limited literature that such patients experience worse patient outcomes. Hospitals may need to revisit their policies regarding outlying patients as increased LOS is associated with an increased likelihood of harm events, worse quality of care and increased healthcare costs.

This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years.